Time series aggregation for optimization: One-size-fits-all?

TL;DR

This paper challenges traditional time series aggregation methods in optimization, proposing output-error based approaches with theoretical support that improve both computational efficiency and accuracy.

Contribution

It introduces output-error based TSA methods with theoretical foundations, demonstrating their potential to outperform traditional clustering approaches.

Findings

Output-error based TSA methods improve optimization accuracy.

Theoretical analysis supports the effectiveness of new TSA approaches.

Enhanced computational efficiency in large-scale optimization models.

Abstract

One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome the computational intractability of these full optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, traditional TSA methods often apply a one-size-fits-all approach based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters - the resulting output error in optimization results - is not well addressed. In this paper, we plan to challenge this belief and show that output-error based TSA methods with theoretical underpinnings have unprecedented potential of computational efficiency and accuracy.